{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MongoDB Atlas\n",
    "\n",
    ">[MongoDB Atlas](https://www.mongodb.com/) is a document database that can be \n",
    "used as a vector database.\n",
    "\n",
    "In the walkthrough, we'll demo the `SelfQueryRetriever` with a `MongoDB Atlas` vector store."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a MongoDB Atlas vectorstore\n",
    "First we'll want to create a MongoDB Atlas VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
    "\n",
    "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `pymongo` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --upgrade --quiet  lark pymongo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "OPENAI_API_KEY = \"Use your OpenAI key\"\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.vectorstores import MongoDBAtlasVectorSearch\n",
    "from langchain_core.documents import Document\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "from pymongo import MongoClient\n",
    "\n",
    "CONNECTION_STRING = \"Use your MongoDB Atlas connection string\"\n",
    "DB_NAME = \"Name of your MongoDB Atlas database\"\n",
    "COLLECTION_NAME = \"Name of your collection in the database\"\n",
    "INDEX_NAME = \"Name of a search index defined on the collection\"\n",
    "\n",
    "MongoClient = MongoClient(CONNECTION_STRING)\n",
    "collection = MongoClient[DB_NAME][COLLECTION_NAME]\n",
    "\n",
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = [\n",
    "    Document(\n",
    "        page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
    "        metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"action\"},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n",
    "        metadata={\"year\": 2010, \"genre\": \"thriller\", \"rating\": 8.2},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
    "        metadata={\"year\": 2019, \"rating\": 8.3, \"genre\": \"drama\"},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
    "        metadata={\"year\": 1979, \"rating\": 9.9, \"genre\": \"science fiction\"},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
    "        metadata={\"year\": 2006, \"genre\": \"thriller\", \"rating\": 9.0},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"Toys come alive and have a blast doing so\",\n",
    "        metadata={\"year\": 1995, \"genre\": \"animated\", \"rating\": 9.3},\n",
    "    ),\n",
    "]\n",
    "\n",
    "vectorstore = MongoDBAtlasVectorSearch.from_documents(\n",
    "    docs,\n",
    "    embeddings,\n",
    "    collection=collection,\n",
    "    index_name=INDEX_NAME,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's create a vector search index on your cluster. In the below example, `embedding` is the name of the field that contains the embedding vector. Please refer to the [documentation](https://www.mongodb.com/docs/atlas/atlas-search/field-types/knn-vector) to get more details on how to define an Atlas Vector Search index.\n",
    "You can name the index `{COLLECTION_NAME}` and create the index on the namespace `{DB_NAME}.{COLLECTION_NAME}`. Finally, write the following definition in the JSON editor on MongoDB Atlas:\n",
    "\n",
    "```json\n",
    "{\n",
    "  \"mappings\": {\n",
    "    \"dynamic\": true,\n",
    "    \"fields\": {\n",
    "      \"embedding\": {\n",
    "        \"dimensions\": 1536,\n",
    "        \"similarity\": \"cosine\",\n",
    "        \"type\": \"knnVector\"\n",
    "      },\n",
    "      \"genre\": {\n",
    "        \"type\": \"token\"\n",
    "      },\n",
    "      \"ratings\": {\n",
    "        \"type\": \"number\"\n",
    "      },\n",
    "      \"year\": {\n",
    "        \"type\": \"number\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating our self-querying retriever\n",
    "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains.query_constructor.base import AttributeInfo\n",
    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
    "from langchain_openai import OpenAI\n",
    "\n",
    "metadata_field_info = [\n",
    "    AttributeInfo(\n",
    "        name=\"genre\",\n",
    "        description=\"The genre of the movie\",\n",
    "        type=\"string\",\n",
    "    ),\n",
    "    AttributeInfo(\n",
    "        name=\"year\",\n",
    "        description=\"The year the movie was released\",\n",
    "        type=\"integer\",\n",
    "    ),\n",
    "    AttributeInfo(\n",
    "        name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
    "    ),\n",
    "]\n",
    "document_content_description = \"Brief summary of a movie\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = OpenAI(temperature=0)\n",
    "retriever = SelfQueryRetriever.from_llm(\n",
    "    llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing it out\n",
    "And now we can try actually using our retriever!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example only specifies a relevant query\n",
    "retriever.invoke(\"What are some movies about dinosaurs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example specifies a filter\n",
    "retriever.invoke(\"What are some highly rated movies (above 9)?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example only specifies a query and a filter\n",
    "retriever.invoke(\"I want to watch a movie about toys rated higher than 9\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example specifies a composite filter\n",
    "retriever.invoke(\"What's a highly rated (above or equal 9) thriller film?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example specifies a query and composite filter\n",
    "retriever.invoke(\n",
    "    \"What's a movie after 1990 but before 2005 that's all about dinosaurs, \\\n",
    "    and preferably has a lot of action\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter k\n",
    "\n",
    "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
    "\n",
    "We can do this by passing `enable_limit=True` to the constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = SelfQueryRetriever.from_llm(\n",
    "    llm,\n",
    "    vectorstore,\n",
    "    document_content_description,\n",
    "    metadata_field_info,\n",
    "    verbose=True,\n",
    "    enable_limit=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example only specifies a relevant query\n",
    "retriever.invoke(\"What are two movies about dinosaurs?\")"
   ]
  }
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